An advanced deep learning predictive model for air quality index forecasting with remote satellite-derived hydro-climatological variables

Abstract

Forecasting the air quality index (AQI) is a critical and pressing challenge for developing nations worldwide. With air pollution emerging as a significant threat to the environment, this study considers seven study sites of the sub-tropical region in Bangladesh and introduces a novel hybrid deep-learning model. The proposed model, expressed as CLSTM-BiGRU, integrates a convolutional neural network (CNN), a long-short term memory (LSTM), and a bi-directional gated recurrent unit (BiGRU) network. Leveraging nineteen remotely sensed predictor variables and harnessing the grey wolf optimization (GWO) algorithm, the CLSTM-BiGRU model showcases its superiority in air quality forecasting. It consistently outperforms the benchmark models, yielding lower forecasting errors and higher efficiency (i.e., correlation coefficient ~1) values. Hence, this study underscores the feasibility and substantial potential of the hybrid deep learning model, which can provide precise forecasts of air quality index, and will be highly useful for relevant stakeholders and decision-makers. Furthermore, the adaptability and potential utility of this innovative model may be ascertained for air quality monitoring and effective public health risk mitigation in urban environments.